Key Responsibilities:

- Design, develop, and implement quantitative models for Treasury Risk and Financial Risk Management.

- Develop and enhance models for:

1. Interest Rate Risk in the Banking Book (IRRBB)

2. Treasury Risk

3. Liquidity Risk Modeling

4. PPNR (Pre-Provision Net Revenue)

5. ICAAP

6. Value at Risk (VaR)

7. PRA110 Liquidity Reporting

- Build econometric forecasting models for balance sheet and income statement projections to support capital planning and business strategy.

- Perform model implementation, calibration, testing, and performance monitoring.

- Participate in model validation and lifecycle management activities from the first line of defense perspective.

- Prepare and maintain comprehensive model documentation, including development methodology, assumptions, limitations, and validation reports.

- Conduct quantitative analysis using large internal and external datasets to identify trends and improve model performance.

- Collaborate with Treasury, Risk Management, Finance, and Technology teams during model development and implementation.

- Ensure compliance with internal model governance policies and regulatory requirements.

- Support model reviews, audits, and regulatory examinations.

- Continuously improve model methodologies and recommend enhancements based on evolving business and regulatory requirements.

Required Skills:

- Strong experience in Quantitative Model Development or Model Validation within the banking or financial services industry.

- Hands-on experience with Treasury Risk Models, including:

1. IRRBB

2. Liquidity Risk

3. PPNR

4. ICAAP

5. VaR Models

- Strong programming skills in Python (mandatory).

- Knowledge of C/C++ or similar programming languages is an added advantage.

- Experience with econometric modeling, statistical analysis, and financial forecasting.

- Strong understanding of banking products, treasury functions, and financial risk management.

- Knowledge of model governance frameworks and regulatory requirements.

- Strong analytical, quantitative, and problem-solving abilities.

- Experience working with large datasets and financial modeling techniques.

Preferred Skills:

- Experience in investment banking, global banking, or financial institutions.

- Familiarity with Basel regulations, capital adequacy frameworks, and liquidity regulations.

- Knowledge of SQL, R, SAS, or MATLAB is an advantage.

- Experience with cloud-based analytics platforms and data visualization tools.

- Understanding of machine learning techniques in quantitative finance is a plus.

Educational Qualification:

- Master's degree or MBA in Economics, Mathematics, Statistics, Finance, Computer Science, or a related quantitative discipline from a reputed Tier-1 institution.